Multitemporal classification of image series with seasonal variability using harmonic components

Multitemporal approaches using sequential data acquired over multiple years are essential for satisfactory discrimination between many land cover classes whose signatures exhibit seasonal trends. At any particular time, the response of several classes may be indistinguishable. Using the estimates of periodogram which are obtained from sequential images through FFT, multiple periodicities of the process have been incorporates into multitemporal classification. The Normalized Difference Vegetation Index (NDVI) was computed for five-day composites of the Advanced Very High Resolution Radiometer (AVHRR) imagery over Texas for 1995-2002 using a dynamic technique.